Search Results (54)

Autonomous vehicles (AVs) hold the potential to revolutionize transportation by improving safety, operational efficiency, and environmental impact. However, ensuring reliability and safety in real-world conditions remains a major challenge. Based on an in-depth examination of 33 peer-reviewed studies (2015–2025), this systematic review organizes advancements across five key domains: fault detection and diagnosis (FDD), collision avoidance and decision making, system reliability and resilience, validation and verification (V&V), and safety evaluation. It integrates both hardware- and software-level perspectives, with a focus on emerging techniques such as Bayesian behavior prediction, uncertainty-aware control, and set-based fault detection to enhance operational robustness. Despite these advances, this review identifies persistent challenges, including limited cross-layer fault modeling, lack of formal verification for learning-based components, and the scarcity of scenario-driven validation datasets. To address these gaps, this paper proposes future directions such as verifiable machine learning, unified fault propagation models, digital twin-based reliability frameworks, and cyber-physical threat modeling. This review offers a comprehensive reference for developing certifiable, context-aware, and fail-operational autonomous driving systems, contributing to the broader goal of ensuring safe and trustworthy AV deployment. Full article

Deep learning (DL) and machine learning (ML) have advanced rapidly. This has driven significant progress in intelligent fault diagnosis (IFD) of bearings. However, methods like self-attention have limitations. They only capture features within a single sequence. They fail to effectively extract and fuse time- and frequency-domain characteristics from raw signals. This is a critical bottleneck. To tackle this, a dual-channel cross-attention dynamic fault diagnosis network for time–frequency signals is proposed. This model’s intrinsic correlations between time-domain and frequency-domain features, which overcomes single-sequence limitations. The simulation and experimental data validate the method. It achieves over 95% diagnostic accuracy. It effectively captures complex fault patterns. This work provides a theoretical basis for better fault identification in bearing–rotor systems. Full article

Intelligent fault diagnosis for rotary machinery often suffers performance degradation under domain shifts between training and deployment environments. To address this, we propose a robust fault diagnosis framework incorporating three key components: (1) an order-frequency-based preprocessing method to normalize rotational variations, (2) a U-Net variational autoencoder (U-NetVAE) to enhance adaptation through reconstruction learning, and (3) a test-time training (TTT) strategy enabling unsupervised target domain adaptation without access to source data. Since existing works rarely evaluate under true domain shift conditions, we first construct a unified cross-domain benchmark by integrating four public datasets with consistent class and sensor settings. The experimental results show that our method outperforms conventional machine learning and deep learning models in both F1-score and recall across domains. Notably, our approach maintains an F1-score of 0.47 and recall of 0.51 in the target domain, outperforming others under identical conditions. Ablation studies further confirm the contribution of each component to adaptation performance. This study highlights the effectiveness of combining mechanical priors, self-supervised learning, and lightweight adaptation strategies for robust fault diagnosis in the practical domain. Full article

Dissolved gas analysis remains the most widely utilized non-intrusive diagnostic method for detecting incipient faults in insulating liquid-immersed transformers. Despite their prevalence, conventional ratio-based methods often suffer from ambiguity and limited potential for automation applicrations. To address these limitations, this study proposes a unified multiclass classification model that integrates traditional gas ratio features with supervised machine learning algorithms to enhance fault diagnosis accuracy. The performance of six machine learning classifiers was systematically evaluated using training and testing data generated through four widely recognized gas ratio schemes. Grid search optimization was employed to fine-tune the hyperparameters of each model, while model evaluation was conducted using 10-fold cross-validation and six performance metrics. Across all the diagnostic approaches, ensemble models, namely random forest, XGBoost, and LightGBM, consistently outperformed non-ensemble models. Notably, random forest and LightGBM classifiers demonstrated the most robust and superior performance across all schemes, achieving accuracy, precision, recall, and F1 scores between 0.99 and 1, along with Matthew correlation coefficient values exceeding 0.98 in all cases. This robustness suggests that ensemble models are effective at capturing complex decision boundaries and relationships among gas ratio features. Furthermore, beyond numerical classification, the integration of physicochemical and dielectric properties in this study revealed degradation signatures that strongly correlate with thermal fault indicators. Particularly, the CIGRÉ-based classification using a random forest classifier demonstrated high sensitivity in detecting thermally stressed units, corroborating trends observed in chemical deterioration parameters such as interfacial tension and CO₂/CO ratios. Access to over 80 years of operational data provides a rare and invaluable perspective on the long-term performance and degradation of power equipment. This extended dataset enables a more accurate assessment of ageing trends, enhances the reliability of predictive maintenance models, and supports informed decision-making for asset management in legacy power systems. Full article

This article addresses the issue of the elevator cabin door drive system failure diagnosis. The analyzed component is one of the most critical and the most vulnerable part of the entire elevator. Existing solutions in the literature include methods such as spectral analysis of system vibrations, motor current signature analysis, fishbone diagrams, fault trees, multi-agent systems, image recognition, and machine learning techniques. However, there is a noticeable gap in comprehensive studies that specifically address classification of the multiple types of system components failures, class imbalance in the dataset, and the need to reduce data transmitted over the elevator’s internal bus. The developed diagnostic system measures the drive system’s parameters, processes them to reduce data, and classifies 11 device failures. This was achieved by constructing a test bench with a prototype cabin door drive system, identifying the most frequent system faults, developing a data preprocessing method that aggregates every driving cycle to one sample, reducing the transmitted data by 300 times, and using machine learning for modeling. A comparative analysis of the fault detection performance of seven different machine learning algorithms was conducted. An optimal cross-validation method and hyperparameter optimization techniques were employed to fine-tune each model, achieving a recall of over 97% and an F1 score approximately 97%. Finally, the developed data preparation method was implemented in the cabin door drive controller. Full article

This study presents a comparative analysis of various machine learning algorithms to evaluate their performance in diagnosing faults within automated manufacturing systems. The primary objective is to identify the most effective model for classifying equipment failures based on historical data. Several algorithms were selected, including support vector machines (SVM), Decision trees, boosting, random forest, k-nearest neighbors (KNN), stacking, and artificial neural networks. The research began with the collection of a dataset using an Arduino-based system with sensors (temperature, electrical current, differential pressure, vibration, and sound) to monitor the equipment’s operational condition. Faults were intentionally induced in a motor, an electrovalve, and a pneumatic cylinder. The data were then processed in a Python environment, undergoing normalization and dimensionality reduction. The models were evaluated through cross-validation and compared using metrics such as precision, recall, F1-score, and accuracy. Results indicated that all models performed well, with the SVM algorithm showing the best overall performance, with an average fault diagnosis accuracy of 91.62% when trained on the full dataset and 66.83% under extreme class imbalance. In contrast, decision trees demonstrated lower generalization ability. This study provides insights for future fault diagnosis research using machine learning and offers recommendations for implementing such technologies in industrial environments. Full article

Transfer learning has emerged as a potent technique for diagnosing bearing faults in environments with fluctuating operational parameters. Nevertheless, the majority of current transfer-learning-based fault diagnosis approaches focus primarily on adapting to varying conditions within the same machine. In real-world applications, there is a frequent need to extend these diagnostic techniques to machines that differ significantly in both function and structural design. Due to the different mechanical structures of different machines, the signal transmission paths are vastly different, and the distribution of collected data varies greatly, making it difficult for existing transfer fault diagnosis methods to meet diagnostic needs. Therefore, a multistep wavelet convolutional transfer diagnostic framework (MSWCTD) is proposed to realize cross-machine bearing fault diagnosis. Firstly, a multistep time shift wavelet convolutional network (MTSWCN) based on the multiscale technique and wavelet transform is proposed to explore the diversity information regarding original vibration data and enhance the feature expression ability. Secondly, a confusion transfer method based on multi-view learning is designed to extract diagnosis knowledge that is transferable, which reduces the discrepancy between machines. Three bearing datasets are utilized to evaluate the MSWCTD, with the MSWCTD showing excellent performance on cross-machine bearing fault diagnosis task. Full article

As electric vehicles (EVs) are growing, the fault diagnosis in their drive motor becomes more important to have optimal performance and safety. Traditional fault detection methods suffer mainly from high false positive and false negative rates, computational complexity, and lack of transparency in decision-making methods. In addition, existing models are also heavy and inefficient. A lightweight framework for fault diagnosis in EV drive motors is presented with the aid of Recursive Feature Elimination with Cross-Validation (RFE-CV), parameter optimization, and in-depth preprocessing. We further optimize the models and their combination to a hybrid Soft Voting Classifier. These techniques were applied to a dataset of 40,040 data entries that had been simulated by a Variable Frequency Drive (VFD) model. We evaluated eight machine learning models, and our proposed Soft Voting Classifier has the highest test accuracy of 94.52% and a Kappa score of 0.9210 on diagnostic performance. Also, the model has minimal memory usage and low inference latency. In addition, Local Interpretable Model-Agnostic Explanations (LIME) were used to improve transparency and gain an understanding of decisions made through the Soft Voting Classifier. Also, the framework was validated by an additional real-world dataset, thereby further confirming its robustness and consistency in performance for different conditions, which indicates the generalizability of the framework in real-world applications. RFE-CV is found to be very effective in feature selection and helps to construct a lightweight and cost-effective ensemble voting model for enhancing fault diagnosis for EV Drive Motors, overcoming its unsatisfactory transparency, accuracy, and computational efficiency. Finally, it contributes to the development of safer and more reliable EV systems through the development of models supervised on fewer features to give the computing time that is a little lighter without compromising its diagnostic performance. Full article

Intelligent fault diagnosis of bearings is crucial to the safe operation and productivity of mechanical equipment, but it still faces the challenge of difficulty in acquiring real fault data in practical applications. Therefore, this paper proposes a domain adaptive meta-relation network (DAMRN) to achieve diagnostic knowledge transfer from laboratory-simulated faults (human-induced faults) to real scenario faults (natural faults) by fusing meta-learning and domain adaptation techniques. Specifically, firstly, through meta-task scenario training, DAMRN captures task-independent generic features from human-induced fault samples, which gives the model the ability to adapt quickly to the target domain tasks. Secondly, a domain adaptation strategy that complements each other with explicit alignment and implicit confrontation is set up to effectively reduce the domain discrepancy between human-induced faults and natural faults. Finally, this paper experimentally validates DAMRN in two cases (same-machine and cross-machine) of a human-induced fault to a natural fault, and DAMRN outperforms other methods with average accuracies as high as 99.62% and 96.38%, respectively. The success of DAMRN provides a viable solution for practical industrial applications of bearing fault diagnosis. Full article

Accurate and rapid diagnosis of fault causes is crucial for ensuring the stability and safety of power distribution systems, which are frequently subjected to a variety of fault-inducing events. This study proposes a novel multimodal data fusion approach that effectively integrates external environmental information with internal electrical signals associated with faults. Initially, the TabTransformer and embedding techniques are employed to construct a unified representation of categorical fault information across multiple dimensions. Subsequently, an LSTM-based fusion module is introduced to aggregate continuous signals from multiple dimensions. Furthermore, a cross-attention module is designed to integrate both continuous and categorical fault information, thereby enhancing the model’s capability to capture complex relationships among data from diverse sources. Additionally, to address challenges such as a limited data scale, class imbalance, and potential mislabeling, this study introduces a loss function that combines soft label loss with focal loss. Experimental results demonstrate that the proposed multimodal data fusion algorithm significantly outperforms existing methods in terms of fault identification accuracy, thereby highlighting its potential for rapid and precise fault classification in real-world power grids. Full article

The operating environment of the shielding sleeve of the main pump motor is complex and changeable, and it is affected by various stresses; so, it is prone to bulging, cracking, and wear failure. The space where it is located is narrow, making it difficult to install additional sensors for condition monitoring. The existing methods have difficulty in taking into account the advantages of multiple aspects, such as the in-depth extraction of multi-scale data features, multi-source data fusion, and attention mechanisms, thus failing to achieve fault diagnosis for the failure of the shielding sleeve. Therefore, this paper proposes a fault diagnosis method for the shielding sleeve based on the attention mechanism and multi-source data fusion. The proposed method is suitable for scenarios where the fault characteristics of single data sources are not obvious and multi-scale and multi-source data need to be fused collaboratively. This method takes the measurable data (torque, rotational speed, voltage, and current) of the main pump motor operation as input signals. First, a multi-scale convolutional neural network based on the attention mechanism (AM-MSCNN) is established to extract rich multi-scale features of the data, and the spatial and channel attention mechanisms are used to fuse the multi-scale features. Then, on the basis of the AM-MSCNN, a convolutional neural network structure based on the attention mechanism for multi-scale and multi-source data fusion (AM-MSMDF-CNN) is proposed to further fuse the primary fusion features of different channels of torque, rotational speed, voltage, and current. Finally, the BP algorithm and the cross-entropy loss function are used to conduct fault diagnosis and classification on the fused features to complete the fault diagnosis of the shielding sleeve failure. To verify the effectiveness of the proposed method, experimental verification was carried out using datasets generated by finite element simulation and a small-scale equivalent prototype. By comparing it to methods such as the one-dimensional convolutional neural network (1D-CNN), Bagging Ensemble Learning, Random Forest, and Support Vector Machine (SVM), it was found that for the simulation data and experimental data, the accuracy of the AM-MSMDF-CNN is 5–10% and 10–15% higher than that of the other methods, demonstrating the superiority of the method proposed in this paper. Full article

Intelligent fault diagnosis (IFD) for mechanical equipment based on small and imbalanced datasets has been widely studied in recent years, with transfer learning emerging as one of the most promising approaches. Existing transfer learning-based IFD methods typically use data from different operating conditions of the same equipment as the source and target domains for the transfer learning process. However, in practice, it is often challenging to find identical equipment to obtain source domain data when diagnosing faults in the target equipment. These strict assumptions pose significant limitations on the application of IFD techniques in real-world industrial settings. Furthermore, the temporal characteristics of time-series monitoring data are often inadequately considered in existing methods. In this paper, we propose a cross-machine IFD method based on a residual full convolutional neural network (ResFCN) transfer learning model, which leverages the time-series features of monitoring data. By incorporating sliding window (SW)-based data segmentation, network pretraining, and model fine-tuning, the proposed method effectively exploits fault-associated general features in the source domain and learns domain-specific patterns that better align with the target domain, ultimately achieving accurate fault diagnosis for the target equipment. We design and implement three sets of experiments using two widely used public datasets. The results demonstrate that the proposed method outperforms existing approaches in terms of fault diagnosis accuracy and robustness. Full article

The increasing use of electric vehicles has made fault diagnosis in electric drive motors, particularly in variable speed drives (VSDs) using three-phase induction motors, a critical area of research. This article presents a fault classification model based on machine learning (ML) algorithms to identify various faults under six operating conditions: normal operating mode (NOM), phase-to-phase fault (PTPF), phase-to-ground fault (PTGF), overloading fault (OLF), over-voltage fault (OVF), and under-voltage fault (UVF). A dataset simulating real-world operating conditions, consisting of 39,034 instances and nine key motor features, was analyzed. Comprehensive data preprocessing steps, including missing value removal, duplicate detection, and data transformation, were applied to enhance the dataset’s suitability for ML models. Yeo–Johnson and Hyperbolic Sine transformations were used to reduce skewness and improve the normality of the features. Multiple ML algorithms, including CatBoost, Random Forest (RF) Classifier, AdaBoost, and quadratic discriminant analysis (QDA), were trained and evaluated using Bayesian optimization with cross-validation. The CatBoost model achieved the best performance, with an accuracy of 94.1%, making it the most suitable model for fault classification in electric vehicle drive motors. Full article

In recent years, intelligent methods based on transfer learning have achieved significant research results in the field of rolling bearing fault diagnosis. However, most studies focus on the transfer diagnosis scenario under different working conditions of the same machine. The transfer fault diagnosis methods used for different machines have problems such as low recognition accuracy and unstable performance. Therefore, a novel multi-task self-supervised transfer learning framework (MTSTLF) is proposed for cross-machine rolling bearing fault diagnosis. The proposed method is trained using a multi-task learning paradigm, which includes three self-supervised learning tasks and one fault diagnosis task. First, three different scales of masking methods are designed to generate masked vibration data based on the periodicity and intrinsic information of the rolling bearing vibration signals. Through self-supervised learning, the attention to the intrinsic features of data in different health conditions is enhanced, thereby improving the model’s feature expression capability. Secondly, a multi-perspective feature transfer method is proposed for completing cross-machine fault diagnosis tasks. By integrating two types of metrics, probability distribution and geometric similarity, the method focuses on transferable fault diagnosis knowledge from different perspectives, thereby enhancing the transfer learning ability and accomplishing cross-machine fault diagnosis of rolling bearings. Two experimental cases are carried out to evaluate the effectiveness of the proposed method. Results suggest that the proposed method is effective for cross-machine rolling bearing fault diagnosis. Full article

This study proposes a fault diagnosis method for harmonic drive systems based on multi-sensor data fusion and the gravitational search algorithm (GSA). As a critical component in robotic arms, harmonic drives are prone to failures due to wear, less grease, or improper loading, which can compromise system stability and production efficiency. To enhance diagnostic accuracy, the research employs wavelet packet decomposition (WPD) and empirical mode decomposition (EMD) to extract multi-scale features from vibration signals. These features are subsequently fused, and GSA is used to optimize the high-dimensional fused features, eliminating redundant data and mitigating overfitting. The optimized features are then input into a support vector machine (SVM) for fault classification, with K-fold cross-validation used to assess the model’s generalization capabilities. Experimental results demonstrate that the proposed diagnosis method, which integrates multi-sensor data fusion with GSA optimization, significantly improves fault diagnosis accuracy compared to methods using single-sensor signals or unoptimized features. This improvement is particularly notable in multi-class fault scenarios. Additionally, GSA’s global search capability effectively addresses overfitting issues caused by high-dimensional data, resulting in a diagnostic model with greater reliability and accuracy across various fault conditions. Full article